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Brief Announcement: Computing Power of Hybrid Models in Synchronous Networks.

Authors: Fraigniaud, Pierre; Montealegre, Pedro; Paredes, Pablo; Rapaport, Ivan; Ríos-Wilson, Martín; Todinca, Ioan;

Brief Announcement: Computing Power of Hybrid Models in Synchronous Networks.

Abstract

During the last two decades, a small set of distributed computing models for networks have emerged, among which LOCAL, CONGEST, and Broadcast Congested Clique (BCC) play a prominent role. We consider hybrid models resulting from combining these three models. That is, we analyze the computing power of models allowing to, say, perform a constant number of rounds of CONGEST, then a constant number of rounds of LOCAL, then a constant number of rounds of BCC, possibly repeating this figure a constant number of times. We specifically focus on 2-round models, and we establish the complete picture of the relative powers of these models. That is, for every pair of such models, we determine whether one is (strictly) stronger than the other, or whether the two models are incomparable.

Countries
France, Germany
Keywords

LOCAL, CONGEST, Broadcast Congested Clique, hybrid model, Theory of computation → Distributed algorithms, [INFO] Computer Science [cs], synchronous networks, 004, ddc: ddc:004

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green